Fluid optical database reconstruction methods and applications thereof

ABSTRACT

A method includes receiving fluid property data of a fluid and receiving material property data for materials in the fluid. The method includes detecting material sensor data from at least one sensor and applying an inverse model and a forward model to the fluid property data and the material property data to provide at least in part synthetic spectral channel data for the materials.

TECHNICAL FIELD

The present description relates in general to fluid optical databasereconstruction methods, and more particularly to, for example, withoutlimitation, fluid optical database reconstruction by integrating largenumber of fluid samples with diverse compositions and properties fromexternal sources and recovering incomplete data through validatedmodeling and testing, and applications thereof.

BACKGROUND

In the field of oil and gas exploration and production, characterizationof formation or wellbore fluid compositions and properties is importantfor reservoir fluid evaluation. For example, reservoir fluid evaluationdeploys formation sampling and testing techniques to collect fluidsamples with minimized contamination and compare with data from existingdatabases to further facilitate early decision making on the economicvalue of potential reservoir exploration, well completion and productionbased on the quality prediction of the fluid compositions andproperties. For example, data bases, such as a fluid optical database isdefined as a database containing fundamental information needed insurface optical fluid analysis and downhole formation sampling andtesting.

The database is often limited by the number of fluid samples,particularly live oils, sample geological distribution over differentfield origins, and fluid types and compositions that represent typicalreservoir fluids encountered during global formation sampling andtesting. Expanding the database through laboratory experiments alone isslow and cost-prohibitive. External databases, e.g., publicallyavailable databases, however, can include large amounts of fluids withdiverse compositions; however, these are often incomplete orinconsistent with respect to all properties and can entirely miss fluidoptical data. Furthermore, the measurements from various laboratories ordata acquisition systems are often gathered with inconsistentmethodologies and may contain ill-posed problems that induce additionalcomplexity for system identification. Because of these combined issues,improving the usefulness of the database may be challenging. Therefore,new and novel database improvement methods are urgently needed.

BRIEF DESCRIPTION OF THE DRAWINGS

The following figures are included to illustrate certain aspects of thepresent disclosure, and should not be viewed as exclusive embodiments.The subject matter disclosed is capable of considerable modifications,alterations, combinations, and equivalents in form and function, withoutdeparting from the scope of this disclosure.

FIGS. 1A and 1B illustrate wellbore systems. FIG. 1A illustrates aschematic view of a logging operation deployed in and around a wellboresystem and FIG. 1B illustrates a schematic view of a wireline loggingoperation deployed in and around a wellbore system.

FIG. 2 shows a schematic overview of an example workflow forreconstructing optical principal spectroscopy component (PSC) andspectral data from an external source (e.g., publically availabledatabase) using available fluid compositional inputs.

FIG. 3 is a flowchart for a fluid optical database reconstruction methodin determining validated testing data from an external source foroptical data reconstruction.

FIG. 4 shows optical absorbance spectra of typical fluid types coveringthe visible (VIS), near infrared (NIR) and mid infrared (MIR) wavelengthrange.

FIG. 5 shows optical transmittance spectra transformed from theabsorbance data of the fluid types shown in FIG. 4 .

FIG. 6 shows principal component analysis (PCA) score values on thefluid types shown in FIG. 4 as a function of a variable index of PSC.

FIG. 7 illustrates empirical multi-input/single-output feedforwardneural network structure applied to simulate each PSC parameter usingfluid compositions and properties as candidate inputs. These candidatesare processed for training variable-input neural networks.

FIG. 8 illustrates a neural network structure for spectrumreconstruction.

FIG. 9A illustrates an embodiment of a fluid optical databasereconstruction method with use of available field optical sensormeasurement data and known fluid sample compositions.

FIG. 9B is a flowchart showing the steps of the embodiment shown in FIG.9A.

FIG. 10A illustrates another embodiment of a fluid optical databasereconstruction method using available field optical sensor measurementdata and unknown fluid sample compositions.

FIG. 10B is a flowchart showing the steps of the embodiment shown inFIG. 10A.

FIG. 11 is a flowchart of alternative fluid spectra feature extractionfor estimating pumpout contamination.

In one or more implementations, not all of the depicted components ineach figure may be required, and one or more implementations may includeadditional components not shown in a figure. Variations in thearrangement and type of the components may be made without departingfrom the scope of the subject disclosure. Additional components,different components, or fewer components may be utilized within thescope of the subject disclosure.

DETAILED DESCRIPTION

A fluid optical database, which contains fundamental information neededin surface optical fluid analysis and downhole formation sampling andtesting, is essential for optical sensor/tool design optimization, fluidanswer product calibration and validation, sensor/tool predictionuncertainty evaluation, and fluid sampling contamination analysis. Atypical existing fluid optical database, for example, might havewide-range fluid optical spectral data measured at standard pressure,volume, and temperature (PVT) conditions with particular types ofspectrometers and fluid compositional and property data obtained frompractical gas chromatography (GC) analysis. As stated above, an improvedor a more complete fluid optical database can be used, e.g., indesigning, optimizing, fabricating and/or manufacturing of amultivariate optical element (MOE) or integrated computation element(ICE). Using the improved database, a user can calibrate an opticalsensor tool, e.g. by updating the calibration file used in a downholetool. In some cases, the improved database may reduce uncertainty in thecalibration or measurement data. Reducing uncertainty can improveconfidence of the measured data or calibration efforts of the downholetool. In some cases, reduced uncertainty may obviate the need for postprocessing, thereby allowing a faster deliverable to the user. In someimplementations, real-time prediction can be afforded at the job site ifthe uncertainty is reduced to a level low enough to mitigate certainassumptions. In some implementations, the improved database can enablebetter predictions of contamination in and around a wellbore site, whichin turn can reduce pump out time, thereby reducing rig time and savingmoney. This is when a downhole tool sensor is used in conjunction withan improved database to determine optimum pump-out time.

The advanced formation sampling and testing with downhole optical toolsin today's application, for example, requires state-of-the-art modelingwith fluid compositions, spectroscopies, and operational sensors data.Current data mining and machine learning technology make modelinganalytics robust and efficient, and demand leveraged databases in bothdata diversity and consistency. However, an internal fluid opticaldatabase is often limited by the number of fluid samples, especiallylive oils, the sample geological distribution, and the fluid types andcompositions that represent typical reservoir fluids encountered inglobal formation sampling and testing. The internal fluid databaserefers to a limited or privately owned database. In some embodiments,the internal fluid database refers to a database that resides on adownhole tool. External databases, on the other hand, may include largenumber fluids with diverse compositions and properties, but often missoptical measurement data. An external database refers to a publicallyavailable database. The construction of a large fluid optical databasewith complete measurement data through conventional methods alone isexpensive. The measurement data obtained from various laboratories ordata acquisition systems may also contain ill-posed problems that induceadded complexity for system identification. Because of these combinedissues, improving the cost-effectiveness of database reconstruction caninclude, among other factors, leveraging a larger and complete databasefrom the internal and external sources, incorporating the simulated datainto the merged structure that lacks measurement data, and validatingdata integration through advanced machine learning and testingprocedures.

The subject disclosure presents a mutual-complementary modeling andtesting method that enables validated mapping from external oil and gasinformation sources to an existing fluid optical database through theuse of forward and inverse neural networks. The forward neural networksuse fluid compositional inputs to produce fluid principal spectroscopycomponents (PSC). The inverse neural networks apply PSC inputs toestimate fluid compositional outputs. The fluid compositional data fromexternal sources can be tested through forward models first. Theproduced PSC outputs are then entered as inputs to inverse models togenerate fluid compositional data. The degree of matching betweenreconstructed fluid compositions and the original testing data suggestswhich part of the new data can be integrated directly into the existingdatabase as validated mapping. It also indicates the degree ofadditional effort needed to overcome the limitation of current forwardand inverse models and to improve future applicability. In practice,these models are used in conjunction with other optical sensor signalstandardization/transformation and fluid spectra deconvolution neuralnetworks to reconstruct an extended fluid optical database with improvedcost-effectiveness.

In one or more embodiments, fluid PSC data can be estimated using knownfluid composition inputs with forward neural network predictive models.This implementation can be sensor independent, making quality datamapping from fluid compositional inputs to generic and full-rangeparameterized PSC outputs powerful. One or more other embodimentsinclude a model validating method to evaluate the uncertainty ofsimulation results on PSC by comparing the consistency of inversionestimated fluid compositional data driven by PSC inputs withcorresponding target fluid data available from external sources. One ormore other embodiments include a fluid spectral reconstruction methodemploying a full-range PSC estimation produced by statistics of a neuralnetwork ensemble. The spectral re-construction method can be implementedwith a visible (VIS), near infrared (NIR) and mid infrared (MIR)spectral deconvolution algorithm respectively using neural networks forcost-effective commercialization.

In some embodiments, a method is provided to optimize optical sensordata to PSC data transformation once field job results from an externalsource (lab results from the third party, for example) become available.After the estimated PSC data are validated by applying forward andinverse neural network models as discussed above, they can be integratedas target outputs of additional reference fluids to update sensor to PSCdata transformation, providing improved robustness.

As described above, feasibility studies are conducted on field data andadditional oil and gas information sources exemplify the application ofusing mutual-complementing and other neural networks for optical tooldatabase reconstruction and testing. The methods disclosed hereininclude data-driven realization for both forward and inverse modeling toevaluate the simulation results in fluid composition and opticalcharacterization and mutual-complementarily validate variousmultivariate calibrations and data mapping. The methods presented inthis disclosure can be applied in real-time formation sampling andtesting, such as prediction modification, uncertainty estimation, andunknown fluid identification. The combination of improved measurementsystems and signal processing can enable a movement toward autonomousdata generation for machine learning applied to downhole optical fluidanalysis with minimized cost.

Some important results of database reconstruction as disclosed hereininclude an ensemble of added simulated spectra of 1,000 fluids from anexternal fluid compositional database to an internal database.Additional results include filling the missing or incomplete fluidcomposition and property data by using simulated spectra data and otheravailable compositional data; refining fluid answer product calibrationwith reconstructed database, which exhibits better fluid diversity, andimproving formation fluid sampling and testing through quality control(QC) enhancement with use of expanded database.

The fundamentals of optical spectrum reconstruction from fluidcompositional databases are supported by the principles of theBeer-Lambert law, by which the optical spectrum of a particularsubstance, which is related to the absorption as a function ofwavelength, can be defined as the sum of the linear responses of entirecompositional concentrations of the substance. However, this correlationhas been widely applied inversely in the practice of using surface anddownhole optical measurements to estimate fluid compositions andproperties. The advancement of machine learning combined with currentdomain knowledge makes data-driven analytics a reliable solution formany problems that are questionable and controversial, including theuncertainty of data consistency between internal and external databases,the replacement of missing data with simulation data, and the validationmethods applied at the different levels of data mining and integration.

Several groups of forward, inverse, and spectrum reconstruction modelsare built in an existing internal database using complete information offluid spectroscopies, compositions, and properties with neural networkensembles (NNEs). A typical existing internal database may haveOptical-PVT full-range fluid spectral data; PSC data that are scorevalues processed with standard PCA routines; and fluid compositional andproperty data. A database from an external oil and gas informationsource, however, may only have fluid composition and property data.

As used herein, a “sensor” refers to an optical sensor or any othersensor as disclosed herein may include at least one or more sensingelements. In some implementations, at least one of the sensing elementsis an ICE designed to measure a fluid characteristic or property.According to some implementations, an ICE is essentially an opticalinterference-based device that can be designed to operate over acontinuum of wavelengths in the electromagnetic spectrum from the visual(VIS) to mid-infrared (MIR) ranges, or any sub-set of that region.

As used herein, the term “optically interact” or variations thereofrefers to the reflection, transmission, scattering, diffraction, orabsorption of electromagnetic radiation either on, through or from oneor more processing elements (i.e., ICE components) or a substance beinganalyzed by the processing elements.

The terms “tool” and “optical sensor” may be used herein interchangeablyand refer generally to a sensor configured to receive an input ofelectromagnetic radiation that has interacted with a substance andproduced an output of electromagnetic radiation from a sensing elementarranged within or otherwise forming part of the optical computingdevice. The sensing element may be, for example, an ICE as describedabove. Prior to field use, the optical computing device, with eachsensing element employed therein, is calibrated such that each outputresponse can be used in conjunction with others to calculate fluidcomposition and properties through various signal transformation andcharacterization models upon being exposed to downhole conditions. Oncecalibrated, the optical computing device can be used in a wellboreoperation, e.g., a drilling operation, a logging operation, a loggingwhile drilling (LWD) operation, a wireline logging operation, anotherconveyance operation, or any other type of activity, such as a measuringwhile drilling (MWD) operation.

FIG. 1A depicts a schematic view of a logging operation deployed in andaround a well system 1100 a downhole in accordance with one or moreimplementations. The well system 1100 a includes a logging system 1108and a subterranean region 1120 beneath ground surface 1106. The wellsystem 1100 a can also include additional or different features that arenot shown in FIG. 1A. For example, the well system 1100 a can includeadditional drilling system components, wireline logging systemcomponents, or other components.

The subterranean region 1120 shown in FIG. 1A, for example, includesmultiple subsurface layers 1122. The subsurface layers 1122 can includesedimentary layers, rock layers, sand layers, or any combination thereofand other types of subsurface layers. One or more of the subsurfacelayers can contain fluids, such as brine, oil, gas, or combinationsthereof. A wellbore 1104 penetrates through the subsurface layers 1122.Although the wellbore 1104 shown in FIG. 1A is a vertical wellbore, thelogging system 1108 can also be implemented in other wellboreorientations, such as horizontal wellbores, slant wellbores, curvedwellbores, vertical wellbores, or any combination thereof.

The logging system 1108 also includes a logging tool 1102, a conveyance1103, surface equipment 1112, and a computer system, also referred toherein as “computing subsystem 1110”. As shown in FIG. 1A, the loggingtool 1102 is a downhole logging tool that operates while disposed in thewellbore 1104. In some embodiments, the logging tool 1102 is suspendedin the wellbore 1104 by the conveyance 1103. The conveyance 1103 can bewireline, slickline, coiled tubing, pipe, a downhole tractor, or acombination thereof that connects the logging tool 1102 to a surfacecontrol unit or other components of the surface equipment 1112. Thesurface equipment 1112 shown in FIG. 1A operates at or above the surface1106, for example, near the well head 1105, to control the logging tool1102 and possibly other downhole equipment or other components of thewell system 1100 a. All or part of the computing subsystem 1110 can beintegrated with one or more components of, the surface equipment 1112,the logging tool 1102, or both.

The computing subsystem 1110 can be embedded in the logging tool 1102(not shown), and the computing subsystem 1110 and the logging tool 1102operate concurrently while disposed in the wellbore 1104. All or part ofthe computing subsystem 1110 may reside below the surface 1106, forexample, at or near the location of the logging tool 1102.

The well system 1100 a includes communication or telemetry equipmentthat allows communication among the computing subsystem 1110, thelogging tool 1102, and other components of the logging system 1108. Thelogging system 1108 can include, but is not limited to, one or moresystems and/or apparatus for wireline telemetry, wired pipe telemetry,mud pulse telemetry, acoustic telemetry, electromagnetic telemetry, orany combination of these and other types of telemetry.

Logging operations are performed in connection with various types ofdownhole operations at various stages in the lifetime of a well systemand therefore structural attributes and components of the surfaceequipment 1112 and logging tool 1102 are adapted for various types oflogging operations.

FIG. 1B depicts a schematic view of a wireline logging operationdeployed in and around a well system 1100 b in accordance with one ormore implementations. The well system 1100 b includes the logging tool1102 in a wireline logging environment. The surface equipment 1112includes, but is not limited to, a platform 1101 disposed above thesurface 1106 equipped with a derrick 1132 that supports a wireline cable1134 extending into the wellbore 1104. Wireline logging operations areperformed, for example, after a drill string is removed from thewellbore 1104, to allow the wireline logging tool 1102 to be lowered bywireline or logging cable into the wellbore 1104.

In some embodiments, a well system 1100 b can include the logging tool1102 in a LWD environment in accordance with one or moreimplementations. Logging operation can be performed during drillingoperations. Drilling is performed using a string of drill pipesconnected together to form a drill string (not shown) that is loweredthrough a rotary table into the wellbore 1104. A drilling rig (notshown) at the surface 1106 supports the drill string, as the drillstring is operated to drill a wellbore penetrating the subterraneanregion 1120. The drill string can include, for example, but is notlimited to, a kelly, a drill pipe, a bottom hole assembly, and othercomponents. The bottom hole assembly on the drill string can includedrill collars, drill bits, the logging tool 1102, and other components.Exemplary logging tools can be or include, but are not limited to, MWDtools and LWD tools.

FIG. 2 shows a schematic overview of a workflow diagram for spectrareconstruction method. As shown in FIG. 2 , the method can include anexisting internal database and an external source, such as an externalfluid compositional database. The existing internal database can furtherinclude, but not limited to, pressure, volume, temperature spectraldata, PSCs and compositional and property data. Similarly, the externalfluid compositional database can include fluid compositional data, whichcan include carbon dioxide (CO₂), methane (C₁), ethane (C₂), propane(C₃), iso-butane (iC₄), n-butane (nC₄), iso-pentane (iC₅), n-pentane(nC₅), hexane plus (C₆₊) where C₆₊ is an approximation of a sum ofchemical concentration of saturates, aromatics, resins, and asphaltenes(SARA), or hexanes (C₆), and heptane plus (C₇₊). In some embodiments,fluid property and environmental parameters include molecular weight(MW), gas-oil ratio (GOR), API gravity, temperature, pressure, anddensity.

In some embodiments, the fluid compositional inputs can be from one ofmore external sources or databases. In other embodiments, the fluidcompositional inputs can be from the existing internal database. In someembodiments, the spectra reconstruction can begin from external sourcesto recover missing PSC and spectral information using simulation resultsproduced from one or more validated model predictions. In otherembodiments, the forward models are used first to produce estimated PSCdata with compositional inputs, such as fluid composition and propertydata as primary inputs from an external source. The inverse models thenapply PSC estimates to generate predictions on fluid compositions andproperties and compare the outputs to the measurement results on thefluid samples obtained from different field origins. The degree ofmatching between predicted fluid compositions and the target data in theexternal source suggests a level of consistency between the internal andexternal databases. It also indicates the direction to update theinternal database and model base, including integrated data selectionand additional effort of refining the current calibration on variousinverse answer product predictive models to improve real-time downholefluid analysis during formation and testing. In other embodiments,spectral reconstruction models take validated PSC and additional inputsfrom forward and inverse models and calculate VIS, NIR, and MIR fluidtransmittance simulated outputs.

The simulation models can be constructed in the internal database andthen introduced as the mutual-complementary testing method that usesforward and inverse models to validate data selection from externalsources for database reconstruction. For example, a fluid compositionaldatabase can be used to demonstrate the developed methods and proceduresand reconstruct the fluid optical spectral data.

FIG. 3 presents a flowchart 100 showing a method for determiningvalidated testing data from external source for optical datareconstruction. The method illustrated in FIG. 3 begins with step 102for applying forward NNE models to simulate PSC data using new fluidcompositional inputs from an external source. In this embodiment, fluidcompositional inputs, such as those described above from an externalsource, are used in conjunction with NNE models. Applying appropriateNNE models with fluid compositional inputs can generate simulated PSCdata.

Once simulated PSC data are obtained, the method further includes step104 for predicting fluid compositions and properties with estimated PSCdata via inverse NNE models. In this step, the PSC data are used asinputs via appropriate inverse NNE models to predict possible fluidcompositions and their related properties.

When the possible fluid compositions and their related properties areobtained, step 106 selects validated testing data from the externalsource that match inverse predictions on the fluid compositions. In thisstep, the predicted possible fluid compositions and their relatedproperties are compared with fluid compositions and/or their propertiesof the testing data that were used as forward model inputs in step 102.When one or more of the fluid compositions and their properties arematched with one or more of the fluid compositions from the externaldata base, the matched fluid compositions and their properties areconsidered validated testing data.

Based on the validated testing data, the method continues with step 108for simulating VIS, NIR and MIR spectra with validated data inputs usingNNE spectra reconstruction algorithms. In step 108, validated PSC dataare used as inputs in conjunction with one or more of NNE spectrareconstruction algorithms to simulate the optical spectra ranging fromVIS, NIR, and MIR spectra. Once simulated VIS, NIR, and MIR spectra areobtained, step 110 is configured for reconstructing optical fluiddatabase by combining validated data from the external source with datain existing database. The reconstruction of the optical fluid databaseis considered achieved when the existing or internal database has addedvalidated fluid compositional data and properties from the externalsource or database with simulated fluid spectra.

In one or more embodiments, a method includes providing fluidcompositional data from a source; introducing the fluid compositionaldata into a computer system; applying at least one of a plurality ofneural network ensemble (NNE) models to simulate principal spectroscopycomponent (PSC) data using the fluid compositional data; predictingfluid compositions and properties, wherein the predicting includesapplying at least one of a plurality of inverse NNE models to thesimulated PSC data; comparing the fluid compositional data withpredicted fluid compositions and predicted properties; selecting a matchbetween the fluid compositional data and the predicted fluidcompositions and the predicted properties; validating matched fluidcompositions and properties as validated testing data; applying at leastone of a plurality of NNE spectra reconstruction algorithms to simulatevisible (VIS), near infrared (NIR), and mid infrared (MIR) spectra usingthe validated testing data; and reconstructing an optical fluid databaseby combining the validated testing data into an existing database.

In some embodiments, the method further includes at least one of:designing of a multivariate optical element (MOE) or an integratedcomputation element (ICE) based on reconstructed optical fluid database;optimizing of an existing MOE or an existing ICE based on thereconstructed optical fluid database; or fabricating of a MOE or an ICEbased on the reconstructed optical fluid database. In some embodiments,the method further includes updating calibration of a downhole opticalsensor based on the reconstructed optical fluid database, and taking anoptical measurement with the calibrated downhole optical sensor. In someembodiments, the method further reducing uncertainty in calibration ormeasurement data based on the reconstructed optical fluid database. Insome embodiments, the method further includes facilitating samplecontamination analysis based on the reconstructed optical fluiddatabase. In some embodiments, the method further includes facilitatinga wellbore operation based on the reconstructed optical fluid database.

In some embodiments, the source is at least one of: a database;laboratory results; or measurements from a tool or sensor. In someembodiments, the fluid compositional data includes at least one of:carbon dioxide (CO₂), methane (C₁), ethane (C₂), propane (C₃),iso-butane (iC₄), n-butane (nC₄), iso-pentane (iC₅), n-pentane (nC₅),and hexane plus (C₆₊) where C₆₊ is an approximation of a sum of chemicalconcentration of saturates, aromatics, resins, and asphaltenes (SARA),or hexanes (C₆), or heptane plus (C₇₊). In some embodiments, the sourceis an external database, and the method further includes expanding theoptical fluid database by merging data from a tool or sensor into theexisting database. In some embodiments, the simulated visible (VIS),near infrared (NIR), and mid infrared (MIR) spectra includes awavelength range from 450 nm to 3300 nm.

In embodiments utilizing the method as described above, forward andinverse models calibrated in the existing internal database for thisapplication can form two groups of mutual complementing models that canvalidate each other. For example, each output parameter in one group canbe estimated from the candidate inputs from the other group. Inverse(composition predictive) models can also be used to evaluate predictionsof forward models for PSC data reconstruction. Forward (PSC predictive)models, on the other hand, can also be used to evaluate predictions ofinverse models for fluid compositional data reconstruction if appliedreversely.

In some embodiments, the forward modeling for optical datareconstruction might be limited by the availability of compositionaldata measurements in an external source. There might be variation intesting results among output variables in each group of models. Decisionon validated sample selection for database reconstruction is based onthe major components of the answer products. Remodeling with use ofreconstructed data and other information is often required to refinecalibrations, and the optical fluid database may grow through iterativedevelopment with inclusion of new measurement and simulation data eachtime. The autonomous data generation might eventually become possiblewhen gaps in database are sufficiently filled with quality measurementand simulation entries.

FIG. 4 shows the absorbance spectra of a few example fluid types,covering a wavelength range from 450 nm to 3300 nm. The fluid typesshown in FIG. 4 include methane, water, dead oil (with only SARAcompositions), live oil (natural oil under reservoir condition withdissolved hydrocarbon gas), condensate, and synthetic drilling fluid(SDF). The absorbance spectra are transformed to transmittance spectrafirst, which are more consistent with the optical sensor detectorresponses and processed with a PCA routine to obtain dimension-reducedPSC data. FIG. 5 shows optical transmittance transformed from theabsorbance data of the fluid types shown in FIG. 4 covering the VIS,NIR, and MIR wavelength range. FIG. 6 displays the PCA score values ofPSC parameters on the same selected samples shown in FIG. 4 . Thedimension-reduced PSC data are used as calibration outputs for forwardmodeling and calibration inputs for inverse modeling. Specifically, thedimension change from 2850 (assuming one nanometer resolution from 450nm to 3300 nm) to 25 (the number of principal components selected)captures more than 99.5% variation of transmittance spectra. As forgeneric optical characteristics, PSC data can be used as calibrationoutputs for forward modeling and as calibration inputs for inversemodeling and spectrum reconstruction modeling.

FIG. 7 illustrates a typical forward model representation for thisapplication using fluid primary compositions and properties as candidateinputs to simulate each PSC output with a standard multilayerfeedforward neural network. The implementation is data-driven andnonlinear in general to ruggedize the mapping between the input andoutput variables that might be noise corrupted and imposed withcomplicated interfering factors. Some of the basic fluid compositionalinputs, for example, for forward modeling, include carbon dioxide (CO₂),methane (C₁), ethane (C₂), propane (C₃), iso-butane (iC₄), n-butane(nC₄), iso-pentane (iC₅), n-pentane (nC₅), hexanes (C₆), and heptaneplus (C₇₊). The fluid property and environmental parameter includemolecular weight (MW), gas-oil ratio (GOR), API gravity, temperature,pressure, and density are also used as candidate inputs, depending onthe data availability and quality in the internal and external database.

The candidate inputs listed previously might not be completely used asthe actual inputs in predicting every PSC parameter. Additionally, eachPSC may not be estimated with single neural network realization withfixed inputs. The simulation algorithm is implemented to create a modelbase for estimating each PSC and to construct an NNE for reducing therisk of inadvertently applying a single neural network that is renderedstatistically inferior as a result of local minimum from initialization,lack of generalization, and other uncertainties associated with networkconfiguration and data partitioning.

FIG. 8 illustrates a neural network structure for spectrumreconstruction. The neural network simulator produces the estimatedvalue of transmittance with desirable resolution for any givenwavelength input specified by the application range. The candidateinputs of the model include PSC parameters and environmental parameters(fluid temperature, pressure, and density). In this exampleimplementation, wavelength (WV) in nanometer scale is used as anadditional variable with other candidate inputs, including PSCparameters and temperature, pressure, and density, to simulate thetransmittance value corresponding to each specific wavelength. AlthoughPSC data set is processed with full-range spectra in modeling, it can beused as cross-band inputs to construct VIS, NIR, and MIR spectrareconstruction models with different NNEs separately, with each NNEalgorithm performing data reconstruction on the focused range. Dependingon the features and reconstruction complexity in each spectral range,the basic neural network structure parameter S1 (the number of neuronson the first hidden layer) and S2 (the number of neurons on the secondhidden layer) is set to 10 and 5 for VIS spectra reconstruction and 20and 10 for NIR and MIR spectra reconstruction, respectively. In someimplementations, the process retains calibration effort again with abackward stepwise input selection routine applied to all candidatevariables except the WV input, which has to be used for each membernetwork to calculate transmittance. For downhole fluid analysis usingoptical sensor measurements, NNE is only built with a smaller number ofmember networks to reduce the computational cost in real-time dataprocessing. For offline database reconstruction, NNE performance can beoptimized by adjusting member network selection, as necessary. It hasbeen demonstrated that once the validated PSC data estimation isjustified, the quality spectrum construction can be achieved with NNEconsisting of only three member networks.

In some implementations, the forward and inverse models developed in theinternal database are applied as candidate member networks to optimizeNNE construction to simulate the missing optical data of an externaldatabase and reconstruct an expanded database with validated mappingfrom the external source. This approach is different from real-timedownhole fluid analysis. While real-time downhole fluid analysisrequires pre-job decisions on model selection and applies predeterminedmodels to estimate unknown reservoir fluid properties, databasereconstruction allows delving into the entire model base to determinethe validated mapping and maximize the compatibility of measurement andsimulation data.

In other implementations, testing data used are obtained from apublically available database, which includes both global geo-PVT andgeochemical fluid information. In such database, the actual C1 and C₆₊data range over approximately 1,000 testing samples from differentreservoirs. Following the workflow illustrated in FIG. 2 , the degree offluid composition consistency in two databases is evaluated first. It isassumed that the validated PSC data estimation from the forward modelcalculation will reproduce matched primary fluid compositions andproperties on the testing data through inverse model simulation, ifmeasurements in both databases are compatible and the simulation modelsare selected properly. To reduce the uncertainty of PSC data estimation,the mutual-complementing model simulation is applied iteratively using agenetic algorithm, which evolutionarily optimizes NNE member networkselection for each predictive model by minimizing the error in finalinverse model predictions on major fluid compositions and properties C₁,C₂, CO₂, C₆₊, density, and API. This can be performed by evolving membernetwork selection from a candidate pool for each NNE in forward PSCcomputation (given the inverse NNEs are unchanged), evolving NNEconstruction for major fluid answer products (given the estimated PSCdata is constant), or evolving member network selection for both forwardand inverse models in a batch optimization mode.

In one or more embodiments, evolutionary optimization with a geneticalgorithm uses binary string code to represent each five-member NNE, andthe optimization is population based to minimize the ranked overallprediction error on a large number of testing data of reproduced majorfluid answer products using genetic operators, such as selection,mutation, and crossover. During evolutionary computation, the update ofPSC ensemble output in each generation is performed by recalculating thearithmetic average based on the member network selection in eachchromosome update, and the forward model computation is not included inthe loop of evolutionary optimization because PSC outputs of testingdata for each candidate member network are already available throughpreprocessing. The ensemble output update for each answer product,however, has to undergo population-based inverse model data processing,which is a function of NNE outputs of forward modeling and a function ofNNE member network selection of inverse modeling.

FIG. 9A illustrates an embodiment of a fluid optical databasereconstruction method. The embodiment shown in FIG. 9A is a variation ofthe embodiment shown in FIG. 3 . The embodiment illustrated in FIGS. 9Aand 9B utilize available lab results similarly to using compositionaldata from an external source; the mutual complementary model validationmethod discussed in previous embodiment from FIG. 3 , therefore, stillapplies. A novel aspect in the embodiment of FIG. 9A is taking estimatedPSC data, if validated, as new reference fluid responses which can beused in conjunction with field sensor data to improve the diversity ofexisting reference fluids, enabling more robust calibration for opticalsensor data transformation.

FIG. 9B presents a flowchart 200 showing the steps of the embodimentshown in FIG. 9A of the fluid optical database reconstruction method.The method illustrated in FIG. 9B begins with step 202 for applyingforward NNE models to simulate PSC data using new fluid compositioninputs from laboratory results (i.e., experimental results). In thisembodiment, fluid compositional inputs, such as those described abovefrom laboratory results, are used in conjunction with NNE models.Applying appropriate NNE models with fluid compositional inputs cangenerate simulated PSC data. The step 202 is different from step 102 inthat step 202 utilizes fluid compositional inputs from laboratoryresults whereas step 102 utilizes fluid compositional inputs from anexternal source.

Once simulated PSC data are obtained, the method further includes step204 for validating fluid compositional prediction with estimated PSCinputs via inverse NNE models. In this step, the PSC data are used asinputs via appropriate inverse NNE models to validate fluidcompositional predictions.

When fluid compositional predictions and their related properties arevalidated, step 206 is directed to modify tool and/or sensor data to PSCdata transformation by including field sample as a new reference fluid.In other words, the fluid compositional data from laboratory results areused for introducing a new reference fluid in the transformation of PSCdata. Based on the transformed PSC data, the method continues with step208 for simulating VIS, NIR, and MIR spectra with validated PSC inputsusing NNE spectra reconstruction algorithms. In step 208, validated PSCinputs are used in conjunction with one or more of NNE spectrareconstruction algorithms to simulate the optical spectra ranging fromVIS, NIR, and MIR spectra. Once simulated VIS, NIR, and MIR spectra areobtained, step 210 is configured for expanding optical fluid databasesby merging data from the field and/or laboratory results with data in anexisting database. The reconstruction and/or expansion of the opticalfluid database is achieved when the existing or internal database hasexpanded to include additional fluid compositional data and propertieswith simulated spectra from the field and/or laboratory results into oneor more existing databases.

FIG. 10A illustrates another embodiment of a fluid optical databasereconstruction method that includes optical tool measurement data andpost-processed model prediction data on field samples into an empiricaldatabase. The focus of this embodiment is to ruggedize PSC estimation byiteratively improving prediction agreement of forward models and opticalsensor data transformation models with unknown fluid composition andproperty data. For instance, the forward NNE models may use modifiedpredictions from initial inverse model outputs in Step 2 to make dataentries self-consistent in Step 3, and transformation model may need tore-calibrate if PSC parameter estimation is updated after Step 3 andvalidated with use of inverse models in Step 2 again.

FIG. 10B presents a flowchart 300 showing the steps of the embodimentshown in FIG. 10A of the fluid optical database reconstruction method.The method illustrated in FIG. 10B includes step 302 for applyingtransformation model to simulate PSC data using field tool and/or sensormeasurement inputs. In this embodiment, field tool and/or sensormeasurement inputs, such as those described above, are used inconjunction with one or more transformation models. Applying one or moreappropriate transformation models with measurement inputs can generatesimulated PSC data. The step 302 is different from step 202 or step 102in that step 302 utilizes field tool and/or sensor measurement inputs,whereas step 202 utilizes fluid compositional inputs from laboratoryresults and step 102 utilizes fluid compositional inputs from anexternal source.

Once simulated PSC data are obtained, the method further includes step304 for predicting fluid compositions and properties with estimated PSCdata via inverse NNE models. In this step, the PSC data are used asinputs via appropriate inverse NNE models to predict possible fluidcompositions and their related properties. At step 306, the methodincludes ruggedizing PSC estimation by improving prediction agreement offorward NNE and transformation models. In this step, improving theprediction agreement between forward NNE and transformation models canprovide higher confidence in simulating PSC data so as to achieve thebest estimated PSC data. Based on the data from step 306, the methodcontinues with step 308 for simulating VIS, NIR and MIR spectra withbest estimated PSC data using NNE spectra reconstruction algorithms. Instep 308, best estimated PSC data are used as inputs in conjunction withone or more of NNE spectra reconstruction algorithms to simulate theoptical spectra ranging from VIS, NIR, and MIR spectra. Once simulatedVIS, NIR, and MIR spectra are obtained, step 310 is configured forcreating an empirical database to save reconstructed field measurementdata for further validation. In this embodiment, the reconstructed fieldmeasurement data can be added to existing databases and additionalvalidation efforts can lead to more ruggedized databases.

In some embodiments, a method includes measuring a sample with anoptical sensor to provide measurement data; introducing the measurementdata into a computer system; applying at least one of a plurality oftransformation models to simulate PSC data using the measurement data;ruggedizing the simulated PSC data by comparing at least one of aplurality of NNE models and the at least one of the plurality oftransformation models to produce an agreement, thereby producing a bestestimated PSC data; applying at least one of a plurality of NNE spectrareconstruction algorithms to simulate VIS, NIR, and MIR spectra usingthe best estimated PSC data; producing reconstructed field measurementdata based on the application of the at least one of the plurality ofNNE spectra reconstruction algorithms; and creating an empiricaldatabase to save the reconstructed field measurement data.

In some embodiments, the method further includes facilitating a wellboreoperation based on the reconstructed field measurement data. In anotherembodiment, the measurement data includes at least one of: carbondioxide (CO₂), methane (C₁), ethane (C₂), propane (C₃), iso-butane(iC₄), n-butane (nC₄), iso-pentane (iC₅), n-pentane (nC₅), and hexaneplus (C₆₊) where C₆₊ is an approximation of a sum of chemicalconcentration of saturates, aromatics, resins, and asphaltenes (SARA),or hexanes (C₆), or heptane plus (C₇₊). In some embodiments, thereconstructed field measurement data is added to an existing database.In some embodiments, the simulated visible (VIS), near infrared (NIR),and mid infrared (MIR) spectra includes a wavelength range from 450 nmto 3300 nm.

The general methods described herein can be applied to, for example, animplementation involving MIR spectra reconstruction and adaptive localregression model for accurately estimating oil-based mud (OBM)contamination and endmember spectral fingerprints. Contaminationestimation of OBM drilling fluid filtrate contamination in petroleum isone of the challenges in obtaining laboratory quality open holeformation tester samples. OBM filtrate contamination includes eitherpetroleum distillate base oil, or synthetic base oil. In someembodiments, the synthetic based oil includes olefins and/or esterswhich are MIR active. MIR optical region from 2450 nm to 3300 nm affordsa high contrast between formation fluid and synthetic drilling fluidfiltrate and petroleum, but that contrast is highly inconsistentdependent on either the synthetic drilling fluid base oil source, or thenature of the petroleum. A measurement accuracy of +/−4 wt % filtratecan be achieved for a generic regression designed to work forOBM/petroleum combinations, which can degrade to +/−6 wt % if the OBMfiltrate is not included in the calibration set. When a calibrationspecifically designed with a near matching drilling fluid filtrate and aclose formation fluid type are applied, the performance improves to 1.1wt % accuracy.

In some embodiments, an adaptive local calibration method includesanalysis of sufficiently similar filtrate and formation fluid pairs. Themethod includes the use of an adaptive neural network to fingerprintboth formation fluids and a set of filtrates. The fingerprint isdesigned to operate simultaneously on the VIS, NIR, and MIR. In someembodiments, the adaptive local calibration method for syntheticdrilling fluid filtrate can include reconstructing MIR spectra ofevolving fluid mixtures during cleanup using real-time multiple sensordata. It also includes using simple linear function or nonlinearartificial neural networks (ANNs) to fingerprint the endmembers, forexample, mud filtrates and clean formation fluids, and optimizing PSCinputs. In some embodiments, the method further includes iterativelyrefining nearest neighbor estimates in real-time synthetic mixing forcalibration construction. This can provide residual estimates forconfidence of calibration. Further, the method is far more efficient andaccurate than a generic global calibration. Therefore, an accuratecontamination estimation can facilitate improvement and improvedestimation of clean formation fluid properties (either spectrafingerprints, geochemistry, or PVT properties) can help in gainingfurther understanding of reservoir architecture, for example,continuity, compartmentalization, fluid grading, etc., among many othertechnical information.

In some embodiments, the spectra reconstruction method is used toreconstruct the spectra (both MIR and VIS spectra) of contaminatedfluids. Based on the evolution of the spectra patterns during thecleanup process, three closest nearest neighbor formation fluids andfiltrates are identified and selected from an existing filtrate andformation fluid spectra library. In some embodiments, an ANN-PSCcalibration can be developed with 9 formation fluid-filtrate sets of the3×3 fluid sets, each from sufficiently high contamination, for example,ranging from 70% contamination to pure formation fluid, for example, 0%contamination. In another embodiment, the PSC level can be automaticallyadjusted to capture the greatest variation of the pumpout trend whileminimizing the residual. A first contamination estimate and pureendmember (filtrate and formation fluid) estimate is then provided. Inother embodiments, a next iteration of 3 closest filtrate and formationfluids are selected to proceed. A new set of three closest nearestneighbor formation fluids and filtrates are then selected and repeateduntil the contamination curve estimate is stable within a desiredtolerance of about 2%.

In some embodiments, the fingerprinted proxy selection of filtrateand/or formation fluid can be accomplished with alternate means, such asbut not limited to, a predetermined number of nearest neighbors. Anobjective of using at least 3 filtrates and 3 formation fluids is toallow simplification of the process of refining the proxy fluidselections and to allow effective interpolation of the calibration curveamong the closest nearest neighbors. Use of many more fluids forcalibration may decrease the accuracy of the analysis, whereas too fewmay provide insufficient calibration.

FIG. 11 presents a flowchart 400 for an embodiment of alternative fluidspectra feature extraction for estimating pumpout contamination. Themethod illustrated in FIG. 11 includes step 402 for collecting operationsensor measurements (measurement data) during pumpout, followed by step404 for converting measurement data to PSC data via neural networktransmation, followed by step 406 for constructing MIR fluid spectrawith PSC inputs using pre-determined PSC to MIR neural network models,followed by step 408 for estimating dominant fluid type by comparingdegree of matching of model produced MIR fingerprint with profiles ofbasic reference oils and mud filtrates, followed by step 410 forselecting nearest M (oils) by N (filtrates) reference data andsimulating mixture absorbance spectra through various linearcombinations at field temperature and pressure, followed by step 412 forrefining sample fingerprint matching with improved estimation of mud/oiltype and degree of contamination, followed by step 414 for comparinggeneric or local synthetic drilling fluid (SDF) model prediction withspectral fingerprint-driven estimation based on the same PSC inputs,followed by step 416 for evaluating MIR and SDF model predictionuncertainty by different PSC inputs and finalizing real-time modelselection, and finally followed by step 418 for building a contaminationtime series curve as a function of volume of pumpout to estimate futurepoint and end memeber values.

In some embodiments, the method includes measuring a sample with anoptical sensor to provide operational sensor measurement data;introducing the operational sensor measurement data into a computersystem; converting, via the computer system, the operational sensormeasurement data to PSC data via at least one of a plurality of neuralnetwork models; constructing MIR fluid spectra with the PSC data usingat least one of pre-determined PSC to MIR neural network models, theconstructed MIR fluid spectra resulting in a MIR fingerprint; estimatinga dominant fluid type by comparing a degree of matching between the MIRfingerprint and spectra of reference oils and mud filtrates; selectingnearest M oils by N filtrates reference data and simulate a mixedspectra at field temperature and pressure; determining the degree ofmatching between the MIR fingerprint and the simulated mixed spectra ofM oils and N filtrates; and formulating a degree of contamination basedon the degree of matching between the MIR fingerprint and simulatedmixed spectra of M oils and N filtrates.

In other embodiments, the method further includes comparing syntheticdrilling fluid (SDF) model with the degree of contamination based on theMIR fingerprint, the simulated mixed spectra of M oils and N filtrates,and the PSC data. In other embodiments, the method includes evaluatingMIR and SDF model prediction uncertainty by different PSC inputs andfinalizing real-time model selection. In other embodiments, the methodfurther includes building a contamination time series curve as afunction of volume of pumpout to estimate future point and end membervalues.

In some embodiments, M and N can be any integer number between 1 to 30,any integer number between 1 to 30, and any integer number between 1 to10. In some embodiments, M and N can be any integer number between 2 and7, any integer number between 2 and 6, any integer number between 3 and5. In some embodiments, M is 3 and N is 3.

In other embodiments, the determining the degree of matching between theMIR fingerprint and the simulated mixed spectra of M oils and Nfiltrates is repeated until the degree of contamination is stable withina desired tolerance of about 2%. In some embodiments, the method furtherincludes facilitating a wellbore operation based on the degree ofcontamination. In some embodiments, the constructed MIR fluid spectraincludes a wavelength range from 2450 nm to 3300 nm.

In other embodiments, the steps for calibration update on newly selectedreference data each time in main embodiment is replaced by mixturespectra simulation at field temperature and pressure. This alternativeapproach further reduces the number of reference spectra with differentmud/oil combinations, and would simplify the nearest neighbor basedfingerprint matching. For PSC input optimization, the alternativeapproach may also use existing model base pre-determined with differentPSC inputs to finalize real-time MIR and SDF model selection. To applythe method in the flowchart 400, all candidate MIR and SDF predictivemodels and reference fluid spectra of basic oils and mud filtrates areimported to a computer system (e.g., a processor or data processor) ofdownhole tool in a wellbore operation and available for model andspectra selection in real-time data processing.

Application of the models described above, such as NNE, forward andinverse neural networks, can be performed on a computer system. Incertain aspects, the computer system may be implemented using one ormore pieces of hardware or a combination of software and hardware,either in a dedicated server, integrated into another entity, ordistributed across multiple entities.

What is claimed is:
 1. A method comprising: receiving fluid propertydata of a fluid; receiving material property data for a material in thefluid, wherein the fluid comprises one or more materials; detectingmaterial sensor data from at least one sensor; and applying an inversemodel and a forward model to the fluid property data and the materialproperty data to provide, at least in part, synthetic spectral channeldata for the materials.
 2. The method of claim 1, further comprising:predicting properties of the fluid based on the at least in partsynthetic spectral channel data provided by applying the inverse modeland the forward model to the fluid property data and the materialproperty data.
 3. The method of claim 1, wherein the material propertydata comprises at least one of physical and chemical properties of thematerial.
 4. The method of claim 1, wherein the material sensor datacomprises optical data to form optical spectra.
 5. The method of claim1, wherein the forward model comprises a set of Neural Network Ensembles(NNE) models.
 6. The method of claim 1, wherein the material comprises afluid material.
 7. A non-transitory, computer-readable medium havinginstructions stored thereon that are executable by a processor toperform operations comprising: receiving fluid property data of a fluid;receiving material property data for a material in the fluid, whereinthe fluid comprises one or more materials; detecting material sensordata from at least one sensor; and applying an inverse model and aforward model to the fluid property data and the material property datato provide, at least in part, synthetic spectral channel data for thematerials.
 8. The non-transitory, computer-readable medium of claim 7,wherein the operations comprise: predicting properties of the fluidbased on the at least in part synthetic spectral channel data providedby applying the inverse model and the forward model to the fluidproperty data and the material property data.
 9. The non-transitory,computer-readable medium of claim 7, wherein the material property datacomprises at least one of physical and chemical properties of thematerial.
 10. The non-transitory, computer-readable medium of claim 7,wherein the material sensor data comprises optical data to form opticalspectra.
 11. The non-transitory, computer-readable medium of claim 7,wherein the forward model comprises a set of Neural Network Ensembles(NNE) models.
 12. The non-transitory, computer-readable medium of claim7, wherein the material comprises a fluid material.
 13. An apparatuscomprising: a processor; and a computer-readable medium havinginstructions stored thereon that are executable by the processor tocause the apparatus to, receive fluid property data of a fluid; receivematerial property data for a material in the fluid, wherein the fluidcomprises one or more materials; detect material sensor data from atleast one sensor; and apply an inverse model and a forward model to thefluid property data and the material property data to provide, at leastin part synthetic spectral channel data for the materials.
 14. Theapparatus of claim 13, wherein the instructions comprise instructionsexecutable by the processor to cause the apparatus to: predictproperties of the fluid based on the at least in part synthetic spectralchannel data provided by applying the inverse model and the forwardmodel to the fluid property data and the material property data.
 15. Theapparatus of claim 13, wherein the material property data comprises atleast one of physical and chemical properties of the material.
 16. Theapparatus of claim 13, wherein the material sensor data comprisesoptical data to form optical spectra.
 17. The apparatus of claim 13,wherein the forward model comprises a set of Neural Network Ensembles(NNE) models.
 18. The apparatus of claim 13, wherein the materialcomprises a fluid material.